class_docstr: MS COCO instance segmentation dataset.
  
  Parameters
  ----------
  root : str, default '~/mxnet/datasets/coco'
      Path to folder storing the dataset.
  splits : list of str, default ['instances_val2017']
      Json annotations name.
      Candidates can be: instances_val2017, instances_train2017.
  transform : callable, default None
      A function that takes data and label and transforms them. Refer to
      :doc:`./transforms` for examples.
  
      A transform function for object detection should take label into consideration,
      because any geometric modification will require label to be modified.
  min_object_area : float, default is 1
      Minimum accepted ground-truth area, if an object's area is smaller than this value,
      it will be ignored.
  skip_empty : bool, default is True
      Whether skip images with no valid object. This should be `True` in training, otherwise
      it will cause undefined behavior.
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docstr: Return pycocotools object for evaluation purposes.
docstr_tok: ["Return","pycocotools","object","for","evaluation","purposes","."]
code: def coco(self):
      """Return pycocotools object for evaluation purposes."""
      if not self._coco:
          raise ValueError(
              "No coco objects found, dataset not initialized."
          )
      if len(self._coco) > 1:
          raise NotImplementedError(
              "Currently we don't support evaluating {} JSON files".format(
                  len(self._coco)
              )
          )
      return self._coco[0]
  
file_path: gluon-cv/gluoncv/data/mscoco/instance.py
imports: from __future__ import absolute_import
  from __future__ import division
  import os
  import numpy
  from PIL import Image
  import mxnet
  from .utils import try_import_pycocotools
  from ..base import VisionDataset
label_desc: Use image node_id 3 for calling the COCOInstance obj's underlying member method code with example usage: obj.coco() and returns: self